CIVB Stock: Is It a Bubble?

Outlook: Civista Bancshares Inc. Common Stock is assigned short-term B3 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Hold
Time series to forecast n: for Weeks²
Methodology : Inductive Learning (ML)
Hypothesis Testing : Chi-Square
Surveillance : Major exchange and OTC

¹The accuracy of the model is being monitored on a regular basis.(15-minute period)

²Time series is updated based on short-term trends.

Summary

Civista Bancshares Inc. Common Stock prediction model is evaluated with Inductive Learning (ML) and Chi-Square^1,2,3,4 and it is concluded that the CIVB stock is predictable in the short/long term. Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses.⁵ According to price forecasts for 3 Month period, the dominant strategy among neural network is: Hold

Key Points

1. Inductive Learning (ML) for CIVB stock price prediction process.
2. Chi-Square
3. Dominated Move
4. Trading Signals
5. Nash Equilibria

CIVB Stock Price Forecast

We consider Civista Bancshares Inc. Common Stock Decision Process with Inductive Learning (ML) where A is the set of discrete actions of CIVB stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.^1,2,3,4

Sample Set: Neural Network
Stock/Index: CIVB Civista Bancshares Inc. Common Stock
Time series to forecast: 3 Month

According to price forecasts, the dominant strategy among neural network is: Hold

F(Chi-Square)^6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Inductive Learning (ML)) X S(n):→ 3 Month $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of CIVB stock

j:Nash equilibria (Neural Network)

k:Dominated move of CIVB stock holders

a:Best response for CIVB target price

Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses.⁵ A chi-squared test is a statistical hypothesis test that assesses whether observed frequencies in a sample differ significantly from expected frequencies. It is one of the most widely used statistical tests in the social sciences and in many areas of observational research. The chi-squared test is a non-parametric test, meaning that it does not assume that the data is normally distributed. This makes it a versatile tool that can be used to analyze a wide variety of data. There are two main types of chi-squared tests: the chi-squared goodness of fit test and the chi-squared test of independence.^6,7

 

For further technical information as per how our model work we invite you to visit the article below: 

How do Predictive A.I. algorithms actually work?

CIVB Stock Forecast (Buy or Sell) Strategic Interaction Table

Strategic Interaction Table Legend:

X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)

Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)

Z axis (Grey to Black): *Technical Analysis%

Financial Data Adjustments for Inductive Learning (ML) based CIVB Stock Prediction Model

1. An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods if, and only if, it is possible without the use of hindsight and the restated financial statements reflect all the requirements in this Standard. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
2. As noted in paragraph B4.3.1, when an entity becomes a party to a hybrid contract with a host that is not an asset within the scope of this Standard and with one or more embedded derivatives, paragraph 4.3.3 requires the entity to identify any such embedded derivative, assess whether it is required to be separated from the host contract and, for those that are required to be separated, measure the derivatives at fair value at initial recognition and subsequently. These requirements can be more complex, or result in less reliable measures, than measuring the entire instrument at fair value through profit or loss. For that reason this Standard permits the entire hybrid contract to be designated as at fair value through profit or loss.
3. For purchased or originated credit-impaired financial assets, expected credit losses shall be discounted using the credit-adjusted effective interest rate determined at initial recognition.
4. At the date of initial application, an entity shall use reasonable and supportable information that is available without undue cost or effort to determine the credit risk at the date that a financial instrument was initially recognised (or for loan commitments and financial guarantee contracts at the date that the entity became a party to the irrevocable commitment in accordance with paragraph 5.5.6) and compare that to the credit risk at the date of initial application of this Standard.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

CIVB Civista Bancshares Inc. Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B3	Ba3
Income Statement	C	B2
Balance Sheet	C	Caa2
Leverage Ratios	C	Ba3
Cash Flow	Baa2	Ba2
Rates of Return and Profitability	Baa2	Baa2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

References

1. Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
2. Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
3. Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
4. A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
5. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
6. Lai TL, Robbins H. 1985. Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6:4–22
7. O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.

Frequently Asked Questions

Q: Is CIVB stock expected to rise?
A: CIVB stock prediction model is evaluated with Inductive Learning (ML) and Chi-Square and it is concluded that dominant strategy for CIVB stock is Hold
Q: Is CIVB stock a buy or sell?
A: The dominant strategy among neural network is to Hold CIVB Stock.
Q: Is Civista Bancshares Inc. Common Stock stock a good investment?
A: The consensus rating for Civista Bancshares Inc. Common Stock is Hold and is assigned short-term B3 & long-term Ba3 estimated rating.
Q: What is the consensus rating of CIVB stock?
A: The consensus rating for CIVB is Hold.
Q: What is the forecast for CIVB stock?
A: CIVB target price forecast: Hold